WO2020057066A1 - Procédé de diagnostic de défaut utilisant une analyse bispectrale de signal de modulation améliorée pour palier à roulement - Google Patents

Procédé de diagnostic de défaut utilisant une analyse bispectrale de signal de modulation améliorée pour palier à roulement Download PDF

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WO2020057066A1
WO2020057066A1 PCT/CN2019/077955 CN2019077955W WO2020057066A1 WO 2020057066 A1 WO2020057066 A1 WO 2020057066A1 CN 2019077955 W CN2019077955 W CN 2019077955W WO 2020057066 A1 WO2020057066 A1 WO 2020057066A1
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Prior art keywords
rolling bearing
vibration signal
msb
noise reduction
fault
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PCT/CN2019/077955
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English (en)
Chinese (zh)
Inventor
甄冬
郭俊超
谷丰收
张琛
张�浩
师占群
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河北工业大学
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Publication of WO2020057066A1 publication Critical patent/WO2020057066A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis

Definitions

  • the invention relates to the technical field of mechanical equipment condition monitoring and fault diagnosis, and in particular to a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis.
  • Rolling bearings are the most widely used mechanical parts in rotating machinery and one of the most vulnerable components.
  • vibration signals of rotating machinery a large number of signals are non-stationary and non-Gaussian distribution signals, especially when faults occur.
  • traditional power spectrum analysis and time-frequency analysis cannot reflect the phase information between frequency components, and generally cannot handle non-minimum phase systems and non-Gaussian signals.
  • Modulation bispectral analysis is used to analyze non-stationary and non-Gaussian signals. Powerful tool. MSB makes up for the shortcomings of second-order statistics that do not contain phase information and has modulation characteristics. Therefore, it is easier to obtain useful fault characteristic information by modulating bispectral vibration signals.
  • MSB can completely suppress Gaussian noise and is powerless to non-Gaussian noise.
  • the existence of these non-Gaussian noises interferes with the higher-order spectrum of the signal, which adversely affects the extraction and analysis of fault features.
  • the mechanical fault signal often contains various noises, and the signal to noise ratio of the signal is generally low, especially when the machine has an early fault, the fault signal is very weak. How to effectively extract the fault characteristic information from the strong noise background directly affects The accuracy of fault diagnosis and the reliability of early fault prediction.
  • an autoregressive (AR) model and MSB are combined to propose a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis.
  • This research idea is derived from the respective characteristics of the two signal analysis methods.
  • the AR model can effectively deal with the non-Gaussian noise in the signal, and the MSB analysis suppresses the Gaussian noise.
  • the technical solution of the present invention to solve the technical problem is to design a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis, and the specific steps are as follows:
  • Step 1 measuring the vibration signal of the detected rolling bearing through a vibration sensor
  • Step 2 Perform an AR model on the obtained vibration signal to perform noise reduction processing to obtain a noise reduction vibration signal x (t);
  • Step 3 Separate the modulation component of the noise reduction vibration signal x (t) with MSB to extract the characteristic frequency of the fault;
  • the present invention has the following beneficial effects:
  • Embodiment 1 is a time-domain waveform diagram of a vibration signal of an inner ring of a rolling bearing according to Embodiment 1;
  • FIG. 4 is a frequency domain diagram of a rolling bearing fault diagnosis method of the rolling bearing inner ring according to the embodiment 1 using the enhanced modulation bispectrum analysis of the present invention
  • FIG. 5 is a frequency domain diagram obtained by using the MSB for the vibration signal of the inner ring of the rolling bearing of Embodiment 1.
  • FIG. 5 is a frequency domain diagram obtained by using the MSB for the vibration signal of the inner ring of the rolling bearing of Embodiment 1.
  • the invention provides a rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis.
  • the specific steps are as follows:
  • Step 1 measuring the vibration signal of the detected rolling bearing through a vibration sensor
  • Step 2 Perform an AR model on the obtained vibration signal to perform noise reduction processing to obtain a noise reduction vibration signal x (t);
  • the step two specifically includes the following steps:
  • Step 101 Determine an appropriate order range of the AR model.
  • the general signal can be taken within 100.
  • Step 103 Compare the kurtosis values of the vibration signals calculated at different orders to find the maximum kurtosis value, and the corresponding order is the optimal order to be determined, and then obtain the noise reduction vibration signal x (t);
  • Step 3 Separate the modulation component of the noise reduction vibration signal x (t) with MSB to extract the characteristic frequency of the fault;
  • the step three specifically includes the following steps:
  • Step 104 In the frequency domain, the MSB of the noise reduction vibration signal x (t) expressed in the form of a discrete Fourier transform X (f) can be defined as:
  • B MS (f c , f x ) represents the bispectrum of the signal x (t)
  • E ⁇ > represents the expectation
  • f c is the modulation frequency
  • f x is the carrier frequency
  • (f c + f x ) and (f c- f x ) are the upper and lower sideband frequencies, respectively.
  • Step 104 MSB of the resulting improvements to modify the carrier frequency f c by eliminating substantial influence component, in order to more accurately quantify sideband amplitude.
  • the improved MSB is MSB-SE, which is defined as follows:
  • Step 106 Calculate the average value of MSB in the f x increment direction to obtain f c slice:
  • ⁇ f represents the resolution of f x .
  • Step 107 Calculate the average value of multiple optimal MSB slices to obtain the fault characteristic frequency of the rolling bearing, which is expressed as:
  • N is the total number of selected f c slices.
  • Step 1 The vibration signal of the inner ring of the rolling bearing is measured by a vibration sensor.
  • the sampling frequency of the vibration signal is 96kHz, the sampling length is 2.880000, and the frequency of the bearing outer ring failure is 65.17Hz.
  • the waveform and frequency domain diagrams of the vibration signal are shown in Figures 1 and 2, respectively. It can be seen that there is a lot of noise and the component of the fault characteristic frequency cannot be extracted.
  • Step 2 Use the principle of maximum kurtosis to adaptively determine the optimal order of the AR model, as shown in Figure 3. By selecting the order of the best AR model, the vibration model is denoised to obtain noise reduction. Vibration signal
  • the third step the noise reduction vibration signal is subjected to MSB separation and modulation components, and the characteristic frequency of the fault is extracted to obtain a frequency domain diagram as shown in FIG. Accurately extracted the fault feature information of the rolling bearing outer ring.
  • the vibration signals of the inner ring of the rolling bearing in Example 1 are compared using MSB.
  • the structure obtained using MSB is shown in Figure 5.
  • the spectrum and noise are mixed, and the effects of harmonics still exist.
  • the method designed by the invention can obtain more accurate results in the diagnosis of rolling bearing faults, and is suitable for popularization and application.

Abstract

L'invention concerne un procédé de diagnostic de défaut utilisant une analyse bispectrale de signal de modulation améliorée pour un palier à roulement, proposé pour remédier à l'inconvénient de l'analyse bispectrale de signal de modulation dans lequel un bruit gaussien peut être supprimé en théorie, mais où les bruits non gaussiens ne peuvent être traités. Le procédé comprend plus précisément : la mesure d'un signal de vibration d'un palier à roulement à l'essai au moyen d'un capteur de vibration ; la réalisation d'un traitement de réduction de bruit sur le signal de vibration obtenu selon un modèle AR, et l'obtention d'un signal de vibration à bruit réduit ; et la réalisation d'une séparation du signal de modulation bispectral sur le signal de vibration à bruit réduit pour obtenir des composantes de modulation, et l'extraction d'une fréquence caractéristique de défaut. Le procédé de diagnostic de défaut utilisant une analyse bispectrale de signal de modulation améliorée pour un palier à roulement extrait efficacement des informations caractéristiques faibles d'un palier défectueux dans un bruit de fond élevé, ce qui facilite la détection précoce des défaillances de paliers.
PCT/CN2019/077955 2018-09-19 2019-03-13 Procédé de diagnostic de défaut utilisant une analyse bispectrale de signal de modulation améliorée pour palier à roulement WO2020057066A1 (fr)

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CN201811097318.0 2018-09-19
CN201811097318.0A CN109029999B (zh) 2018-09-19 2018-09-19 基于增强调制双谱分析的滚动轴承故障诊断方法

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CN111207926B (zh) * 2019-12-27 2022-02-01 三明学院 一种基于滚动轴承故障诊断方法、电子装置及存储介质
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CN114778114B (zh) * 2022-04-01 2022-11-22 西南交通大学 一种基于信号冲击性和周期性的轴承健康指标构建方法

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